Principles of Data Mining
by Max BramerFrom the reviews of the second edition: Data Mining, the automatic extraction of implicit and potentially useful information from data, is increasingly used in commercial, scientific and other application areas.
Principles of Data Mining explains and explores the principal techniques of Data Mining: for classification, association rule mining and clustering.
Each topic is clearly explained and illustrated by detailed worked examples, with a focus on algorithms rather than mathematical formalism.
It is written for readers without a strong background in mathematics or statistics, and any formulae used are explained in detail.
This second edition has been expanded to include additional chapters on using frequent pattern trees for Association Rule Mining, comparing classifiers, ensemble classification and dealing with very large volumes of data.
Principles of Data Mining aims to help general readers develop the necessary understanding of what is inside the 'black box' so they can use commercial data mining packages discriminatingly, as well as enabling advanced readers or academic researchers to understand or contribute to future technical advances in the field.
Suitable as a textbook to support courses at undergraduate or postgraduate levels in a wide range of subjects including Computer Science, Business Studies, Marketing, Artificial Intelligence, Bioinformatics and Forensic Science.
Very easy to read and follow book for beginners to the subject. I have tried many other texts to get introduced to data mining, all of other texts discuss theories in abstract way without giving helpful examples.
Unlike most technical books, this is very well written. Generally it was very clear and the information was presented in an interesting way.
The author has somehow written a detailed, insightful book on decision trees without any serious mathematics, using only simple examples. Each algorithm is introduced through a small example.
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